论文标题
中心界的本地图像混合物用于对比表示学习
Center-wise Local Image Mixture For Contrastive Representation Learning
论文作者
论文摘要
基于实例歧视训练模型的对比学习，以将锚样本的不同转换与其他样品区分开，这不考虑样本之间的语义相似性。本文提出了一种新型的对比学习方法，名为Clim，该方法使用了数据集中其他样本的阳性。这是通过搜索锚点的本地类似样本并选择靠近相应群集中心的样品来实现的，我们将其表示为中心局部图像选择。选定的样品是通过数据混合物策略实例化的，该策略的性能是平滑的正则化。结果，高升以强大的方式鼓励当地的相似性和全球聚集，我们发现这对特征表示有益。此外，我们介绍了\ emph {多分辨率}增强，这使表示形式不变。我们达到75.5％的TOP-1精度，而Resnet-50的线性评估则达到了59.3％的TOP-1准确性，而仅使用1％的标签进行微调。
Contrastive learning based on instance discrimination trains model to discriminate different transformations of the anchor sample from other samples, which does not consider the semantic similarity among samples. This paper proposes a new kind of contrastive learning method, named CLIM, which uses positives from other samples in the dataset. This is achieved by searching local similar samples of the anchor, and selecting samples that are closer to the corresponding cluster center, which we denote as center-wise local image selection. The selected samples are instantiated via an data mixture strategy, which performs as a smoothing regularization. As a result, CLIM encourages both local similarity and global aggregation in a robust way, which we find is beneficial for feature representation. Besides, we introduce \emph{multi-resolution} augmentation, which enables the representation to be scale invariant. We reach 75.5% top-1 accuracy with linear evaluation over ResNet-50, and 59.3% top-1 accuracy when fine-tuned with only 1% labels.
